EP2460252A2 - Système pour planifier la charge et la décharge d une batterie - Google Patents

Système pour planifier la charge et la décharge d une batterie

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Publication number
EP2460252A2
EP2460252A2 EP10805050A EP10805050A EP2460252A2 EP 2460252 A2 EP2460252 A2 EP 2460252A2 EP 10805050 A EP10805050 A EP 10805050A EP 10805050 A EP10805050 A EP 10805050A EP 2460252 A2 EP2460252 A2 EP 2460252A2
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EP
European Patent Office
Prior art keywords
cells
battery
battery cells
group
discharging
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Application number
EP10805050A
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German (de)
English (en)
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EP2460252B1 (fr
EP2460252A4 (fr
Inventor
Hahnsang Kim
Kang G. Shin
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University of Michigan System
University of Michigan Ann Arbor
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University of Michigan System
University of Michigan Ann Arbor
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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/90—Regulation of charging or discharging current or voltage
    • H02J7/92—Regulation of charging or discharging current or voltage with prioritisation of loads or sources
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H01M10/441—Methods for charging or discharging for several batteries or cells simultaneously or sequentially
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/50—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries acting upon multiple batteries simultaneously or sequentially
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/50—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries acting upon multiple batteries simultaneously or sequentially
    • H02J7/575—Parallel/serial switching of connection of batteries to charge or load circuit
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/50—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries acting upon multiple batteries simultaneously or sequentially
    • H02J7/585—Sequential battery discharge in systems with a plurality of batteries
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries
    • H02J7/855—Circuit arrangements for charging or discharging batteries or for supplying loads from batteries with circuits adapted for supplying loads from the battery
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present disclosure relates to a system for scheduling battery charge and discharge that employs scheduling policies that allow battery cell activities to adapt to load demands and the conditions of individual cells.
  • battery management needs 1 ) cell balancing and 2) the prevention of the overcharge and deep-discharge of battery cells. Since characteristics of individual battery cells are not the same, strong cells can put stress on weak cells, and vice versa. Cell balancing is also very important for battery health. On the other hand, when a lithium-ion cell (that has high electrical energy concentrated in a small volume in the cell) is overcharged, active materials therein will most likely react with other materials and electrolytes, potentially causing an explosion, let alone damaging the cell itself. When the cell is deep-discharged, or it continues to be discharged, despite its terminal voltage below a certain threshold called the cutoff voltage, it may become short-circuited, transitioning the cell into an irreversible condition.
  • the cutoff voltage a certain threshold
  • a lithium-ion cell When the cells are connected in parallel it is important to balance their voltages, since their interactions and dependencies make their voltages drift apart. Higher-voltage cells may then inversely charge the lower- voltage cells, causing the entire terminal voltage to drop from the desired value of the parallel connected cells. Moreover, a lithium-ion cell has unique characteristics, such as discharge efficiency (the higher the discharge rate, the lower the deliverable capacity), and recovery efficiency (the interface- concentrated gradient inside the cell is diffused during a Vest,' after which the cell can be charged with large electric current over a short time).
  • a scheduling framework should operate reasonably well in all circumstances. That is, using the framework, one should be able to extend a battery cell's operation-time as much as any other scheduling mechanism can.
  • 'operation-time we mean the cumulative time of the charge current drawn from a battery cell until the cell no longer delivers the required charge current to applications. That is, the operation-time ends when the terminal voltage of the cell falls below the cutoff voltage.
  • a battery management system for managing a plurality of battery cells arranged in reconfigurable circuit paths.
  • the battery management system includes a scheduling module that receives an estimated load demand for the plurality of battery cells and determines a subset of the plurality of battery cells needed to meet the load demand, wherein the number of battery cells in the determined subset is inversely correlated to a rate at which the plurality of battery cells recover voltage in a rest state.
  • the schedule module further controls a plurality of switches that selectively interconnect the battery cells to connect the subset of battery cells to discharge terminals.
  • a method for scheduling battery cells for charging and discharging in a reconfigurable battery system.
  • the method includes: monitoring state of charge for each of a plurality of battery cells; partitioning the plurality of battery cells into a group of cells eligible for discharging and another group of cells eligible for charging in accordance with the state of charge of the battery cells; charging one or more battery cells in the group of cells eligible for charging while one or more battery cells in the group of cells eligible for discharging are being discharged; and reassigning the plurality of battery cells into either the group of cells eligible for discharging or the group of cells eligible for charging after the step of charging one or more battery cells.
  • Figure 1 is a diagram depicting an exemplary battery management system for scheduling battery charge and discharge
  • Figure 2 is a graph illustrating voltage recovery with respect to different discharge rates
  • Figure 3 is a diagram depicting an arrangement by which battery cells are bypassed for cell balancing
  • Figure 4 is a flowchart of an exemplary embodiment of the scheduler in the battery management system
  • Figure 5 is a flowchart of another exemplary embodiment for the scheduler in the battery management system;
  • Figures 6A-6C are graphs illustrating the relationship between recovery efficiency and other battery cell characteristics;
  • Figure 7 is a graph illustrating the relationship between the amount of battery cells connected in series and the amount of parallel connected groups of battery cells for a reconfigurable battery circuit delivering a set supply voltage
  • Figures 8A-8C are charts comparing cell balancing periods for different discharge policies
  • Figure 9A-9C are graphs comparing discharge distribution for different discharge policies
  • Figures 1 OA and 1 OB are graphs illustrating a comparison between the voltage changes with Dualfoil and a model of a disclosed embodiment
  • Figure 1 1 is a graph illustrating comparisons of the operation times of various scheduling methods.
  • Figure 12 is a graph illustrating the benefits of a dynamic threshold for switching a battery into G L
  • Figure 1 depicts an exemplary battery management system 10.
  • the battery management system 10 is comprised an adaptive filter module 12, a scheduler module 14 and a plurality of battery cells 18a-18n arranged in reconfigurable circuit paths.
  • a battery cell can refer to a single battery cell, a series chain of battery cells, a battery module, or a battery pack.
  • the plurality of battery cells 18 are selectively interconnected to charger terminals 4, discharge terminals 6 and/or to each other by a plurality of switches (not shown).
  • An exemplary arrangement for a reconfigurable battery cell circuit is described in U.S. Patent Application Serial No. 12/757,293 filed on April 9, 2010 and incorporated herein by reference. Other types of reconfigurable circuit arrangements are also within the scope of this disclosure.
  • the input to the adaptive filter 12 is a history of the loads measured at certain intervals.
  • An estimate of the upcoming load demand is output by the adaptive filter to the scheduler 14.
  • the schedule 14 is responsible to solve for a group threshold, ⁇ G , with which to partition the cells into two groups and determine, k, the number of cells in one group of cells which is to be discharged.
  • ⁇ G group threshold
  • k the number of cells in one group of cells which is to be discharged.
  • the scheduler 14 implements a main scheduling program.
  • the main scheduling program involves offline and online procedures.
  • the program creates the reference to the battery characteristics via the charge-and-discharge cycle; this cycle may repeat several times.
  • the reference describes the average cell characteristics that include the voltage recovery factor (v), the terminal voltage, and the cutoff voltage.
  • the program may update the reference since the physical condition of cells changes over time as long as the charge-and- discharge cycle is repeatedly scheduled.
  • the update of v is periodic, effectively offsetting a cell's aging effect.
  • the adaptive filter estimates an upcoming load demand. The estimating may incur a high computational complexity. Also, the load demand may fit into a large time scale, e.g., on the order of minutes.
  • a scheduling ratio ( ⁇ ) is created by which the scheduling as a subroutine may run faster than or as slow as the estimating.
  • the subroutine includes monitoring SoC levels, partitioning the battery-cell array, determining G k for discharge and G c , for charge, and manipulating the battery circuit for either discharge or charge, or both.
  • the frequency of the profiling depends upon a residual time (i.e., the period of time elapsed after the last execution) and the battery age.
  • the battery age is critical in the sense that the older the battery, the higher the unpredictability in battery dynamics. So, the profiling is required more frequently for older batteries.
  • the frequency is highly subject to battery chemistry, generally speaking, for lithium-ion batteries for EVs, biweekly profiling is expected for more than 5-year old batteries, while monthly profiling for less than 5-year old ones.
  • the adaptive filter 12 functions to estimate upcoming load demand for the battery cells based on previous load demand measures.
  • RLS recursive least-squares
  • a system overhead is directly associated with ⁇ t .
  • a fundamental principle in reducing the overhead is that the higher the predictability, the lower the frequency of the estimation in ⁇ t .
  • the accuracy in the predictability is assessed with the result of Eq. (2). That is, when an error is negligible.
  • a battery cell's voltage is partly proportional to its state-of- charge (SoC) level that can be determined by counting coulombs.
  • SoC state-of- charge
  • a lower-voltage cell can be at a higher-SoC level than a higher-voltage cell, due to the discharge and recovery efficiency. This discordance suggests that some cells whose voltages fall below the cutoff voltage still have a sufficient charge current to deliver, making it essential to schedule the charge, discharge, and rest activities.
  • the scheduler 14 manages all cells in the array in accordance with their SoC level, partitioning it into two groups: (1 ) G H in which the member cells' SoC level is higher than ⁇ G , and (2) G L in which the member cells' SoC level is lower than or equal to, ⁇ G . Also, when an individual cell's voltage is below the cutoff voltage, the cell is put into G L . There are two reasons for this partitioning. First, the scheduler 14 prevents cells from being deep-discharged, which may otherwise cause an irreversible damage to the cells.
  • G L plays a buffering role to reserve energy, allowing only a low load demand to be met.
  • the scheduler 14 allows cells to be charged and discharged simultaneously. Cells in G L can be charged while those in G H are being discharged.
  • ⁇ G must be dynamically adjusted to the load. This dynamic adjustment is made as
  • NC is the nominal capacity of all of the cells in the array.
  • Esoc(G) return the average SoC of elements in G;
  • the scheduler sets ⁇ G based on equation (7).
  • the value of ⁇ G is updated during the partitioning.
  • the SoC level of any cell in G H is lower than ⁇ G , the corresponding cells are put into G L.
  • G H ⁇ , i.e., no cells available to be discharged, some cells in G L are put back into G H with a decreased value of ⁇ G .
  • ⁇ G is linearly decreased by a ⁇ 1.
  • a flag, Fs, is turned on, indicating that the SoC level of all cells is too low to meet a high load demand.
  • the scheduling mechanism is switched to a parallel scheduling method (nRR ) from a weighted-k round-robin scheduling method (kRR ).
  • nRR parallel scheduling method
  • kRR weighted-k round-robin scheduling method
  • the load demand, d is to be shared primarily among the cells in G H - Selecting an appropriate value of k is crucial since the more cells become available, the lower the per cell load.
  • k is within the range, when the per-cell load is too low (i.e., v > d/k and k > 2), k is decreased by 1 , thus increasing the per-cell load.
  • v is in the form of an array, k also becomes an array. This way, k is determined to exploit the recovery efficiency and prevent each cell from excessive discharge.
  • the scheduler 14 selects k cells with the highest SoC level in G H , referred to as the kRR scheduling, and then determines the per-cell load. Each cell / shares d, weighted by
  • the 1 RR scheduling is effective for balancing the SoC level and voltage of the cells, while it may cause excessive per-cell discharge and the overhead for frequently switching from one cell to another.
  • this mechanism is referred to as sequential (1+ 1 RR) scheduling.
  • the 1 + 1RR scheduling has nothing to do with voltage-balancing, but it is vulnerable to excessive per-cell discharge, unable to exploit the battery characteristics.
  • this mechanism is referred to as parallel (nRR) scheduling.
  • the nRR scheduling is robust against excessive per-cell discharge, although it fails to exploit the battery characteristics.
  • the kRR scheduling acts as 1 RR at a low discharge rate and as nRR at a high discharge rate, achieving the merits of all. Note that the kRR scheduling subsumes all of these three scheduling mechanisms.
  • N(G) return the number of elements in G
  • H(/c, G) return /c cell(s) with the highest SoC level in G;
  • Figure 4 depicts an exemplary embodiment of these principles by the scheduler 14.
  • the scheduler 14 continually or periodically monitors the state-of-charge for each cell in the plurality of battery cells 18 as indicated at step S1 10.
  • the scheduler 14 partitions the battery cells into a group of cells eligible for discharging G H and another group of cells eligible for charging G L using a cutoff voltage in the manner described above.
  • the scheduler 14 determines k at step S1 14.
  • the scheduler 14 configures the reconfigurable battery circuit accordingly at step S1 16. More specifically, the schedule 14 selects k cells from G H and connects the cells to the discharge terminals, where the cells can be discharged at step S118. Remaining battery cells may be connected to the charge terminals, where these cells can be concurrently charged as further described below. This process is repeated at predetermined time intervals.
  • FIG. 5 An alternative embodiment of the scheduler 14 is shown in Figure 5.
  • the scheduler 14 may repartition and reconfigure the topology multiple times for every determination of d * .
  • d * is estimated and k is determined at step S120. Once k is determined then a counter may be set at step S120.
  • the counter may be set to a hard coded parameter or can be dynamically calculated in accordance with the estimated load demand. For instance, when the variation of the load demand is high, then k may be calculated more frequently.
  • step S124 The state-of-charge of each battery cell is monitored at step S124.
  • the scheduler 14 then partitions the battery pack at step S126 and configures the reconfigurable battery circuit accordingly at step S128.
  • Battery cells are then discharged during a specified time interval as indicated at step S130. Once the time period lapses, the counter is decremented at step S132. Steps S124 to S134 are repeated until the counter is fully decremented. Once fully decremented, processing continues at step S120.
  • the estimation of a load demand may be costly in comparison to partitioning the battery cells. Accordingly, the estimation of d * and calculation of k may be performed at certain time intervals, and the remaining tasks may be performed more frequently.
  • the scheduler 14 is also responsible to schedule charging of the battery cells. For the charging process, priority is given to the cells in G L . The cell with the lowest SoC level is selected and charged until its SoC level reaches the average SoC level of the cells in G H - The charged cell is then put into G H . This procedure, called quick average charging, repeats until no cell is left in G L . This way, undesirable cases, such as voltage-imbalances at any point in time, are avoided. After all cells in G L are charged, the cells in G H start to be charged. Unlike the way applied in G L , all the cells in G H are charged at the same time.
  • the reason for this is that despite the unpredictable disruption of the charging process, the cells should be available to be discharged without the concern of voltage-imbalances. To prevent any cells from being overcharged, the charging process stops whenever any of the cells in G H is fully charged. Then, individual cells with a lower-SoC level are separately charged.
  • V n nominal voltage
  • ⁇ -(k, G) return /c cell(s) with the lowest SoC level in G;
  • the quick average charging is resilient against overstresses that occur to the cells experiencing an intensive charge-and-discharge cycle.
  • a simple policy is used where individual cells in G L are deferred from their transfer to G H until they are fully charged.
  • a just-fully-charged, weak cell- note that a weak cell may be charged and/or discharged faster than healthy cells-is likely selected first for discharge due to its higher-SoC level, and then continues being discharged until its SoC level reaches the average SoC level of the cells in G H .
  • the weak cell's SoC level falls below ⁇ faster than that of the healthy cells which is also close to ⁇ G , thus transferring the weak cell back to G L . Then, the weak cell is most likely selected for charge again. As a consequence, the cycle of charging and discharging the weak cell makes it weaker, eventually leading to its failure.
  • the SoC of series-connected battery cells is important to keep balanced. As mentioned earlier, the cycle of charging and discharging real-life cells makes their characteristics drift apart. This development is caused by several reasons including possible defects in cell manufacturing, packaging, and management. For this reason, cell balancing in battery management helps extend the operation time of the entire pack and mitigate the stress imposed on individual battery cells, ultimately resulting in the improvement of their state of health.
  • One method for cell balancing is to bypass battery cells with a lower SoC. As shown in Fig. 3, cells with a lower SoC are bypassed or only cells with a higher SoC are connected to the load. In this method, a question about how much to stay the cells connected to, or bypassed from, the load must be answered. Three exemplary timing policies are described below. Battery cells can be bypassed using the reconfigurable battery cell circuit noted above as well as other arrangements.
  • SoC 1 ⁇ SoC 2 ⁇ ... ⁇ SoC 1n is cell /s current voltage.
  • d w and v are relatively constant during a cycle of cell balancing.
  • adjacent battery cells independently operate to each other.
  • a bottom-up (BU) policy is a policy that cell 1 with the lowest SoC is bypassed first after the time period, At 1 .
  • the time period, At 1 is determined, based on a load demand, i.e., a discharge rate.
  • a top-down (TD) policy is a policy that cell m with the highest SoC is exclusively connected for At m in the beginning. After the discharge of NC-(SoC n - SoC 1 ) from cell m , cell m - ⁇ is then exclusively connected. The discharging cascades down to cell 1. Thus, a discharge rate d
  • a median (MD) policy is a policy that all cells but the lowest-SoC one are connected to the load. The connected cells are discharged until the total discharge equals the median of precedent levels of SoC, i.e., SoC c that minimizes E( SoC ⁇ -SoC c ), where ⁇ j ⁇ m and E is the mean.
  • the median is computed for the connected cells (i.e., excluding cell having lowest SoC.
  • the connected cells are discharged for a period of time that is computed as a function of the median, SoC c . More specifically, the time period during which cell j is connected is
  • the MD policy limits the number of cells to be connected to the load. This way allows the SoCs of the entire cells to converge in the long run, and the stress imposed on the individual cells to be mitigated.
  • the BU and TD policies have the same period of scheduling i.e.,
  • the BU policy can impose on individual cells less stress than the TD policy.
  • the BU policy can impose on individual cells less stress than the TD policy.
  • the difference in SoC is checked across the series-connected cells.
  • a threshold i.e., tut iH SoC 1 , SoCi ,• - . ⁇ oC,,-, ⁇
  • the shape of voltage-drop curves may differ in different kinds of a battery, the voltage-drop curves of a battery for different discharge rates are very similar or even the same. That is, increasing the discharge rate by 1 C decreases the operation-time by 50%. Given the curve- shape similarity, the relationship between the voltage-drop curves with respect to the operation-time can be derived.
  • a nonlinear voltage curve at a constant discharge rate iC is defined as an invertible function, F/. t ⁇ V.
  • F ref the constant discharge rate C as a reference.
  • the operation-times associated with F ref correspond to those with F 1 , based on their shape similarity:
  • F ⁇ l F ref can be approximated to be a set of linear functions n — I : ⁇ I- ⁇ - - ⁇ ⁇ * - ⁇ ? - ' * " - T f ⁇ ( - where J > (' ⁇ " ⁇ ' + ty is differentiated by the discharge rate. For instance, given two points in the operation-time domain with a discharge rate iC, two corresponding points in the operation-time domain are obtain via ⁇ - These two points are then applied to F ref , yielding a voltage drop.
  • a battery cell has a limited charge recovery effect at a high discharge rate; a high load for a short period of time causes a high interface- concentrated gradient among electro-active species inside the cell, making the usable charge temporarily unavailable due to the lag between reaction and diffusion rates.
  • the recovery efficiency when the cell is allowed to >est' for some time at a low (or zero) discharge rate, the voltage that dropped temporarily goes back up, referred to as the recovery efficiency.
  • the recovery effect depends on the discharge rate, the discharge time, and the rest time.
  • the recovery effect is proportional to the relationship, however, is not linear, as shown in Figure 4A, because the diffusion process occurs even during the course of the low-discharge activity. From the recovery-efficiency curve, one can find local optimal discharge rates that maximize the recovery efficiency. For instance, when the cell is discharged at 0.8261 C or 2.0435C, its recovery effect is locally maximal.
  • the discharge time over which the cell is continuously discharged has a similar effect on the recovery efficiency as shown in Figure 4B. For instance, when the cell is discharged for 5 or 13 minutes at 0.8261 C, the recovery efficiency is locally maximal.
  • Figure 4C shows the average cumulative recovery rate with respect to the rest time. 70% of the dropped voltage is recovered within one minute, and 85% within two minutes.
  • a discharge profile is defined as workload, p, over the operation-time, i.e., a sequence of variable loads, ds, required.
  • ds are approximated by piece-wise constant loads, i.e., represented by a set of M levels of the discharge rate, (/i,...,/ m ), where M is used to characterize d, given At that is a fraction of the total operation-time, T.
  • the goal is to extend a battery pack's operation-time and lifetime - defined as the duration during which the pack provides required (converted) electrical energy to the load while each of its cells repeats the charge-and-discharge cycle - by effectively scheduling charge, discharge, and rest activities.
  • the metrics used include the battery pack's operation-time and usability, and the reduction in voltage-imbalance between parallel-connected cells in the pack.
  • a battery management emulator is designed that includes the four scheduling mechanisms, discharge profile, and battery-activity profiling.
  • the battery management emulator uses the discharge profile, p, specified in equation (19). In p, the random discharge y is normally distributed as
  • di 0.4C x ⁇ f which is a lower bound of p.
  • the upper bound, d u , of p is set to 4.3 C x ⁇ f.
  • d m0 ⁇ and d(0) to d u , and d ⁇ respectively.
  • the battery management emulator simulates the cell discharge according to each scheduling mechanism.
  • the battery pack is assumed to contain 4 parallel-connected cells, but it can be extended in various ways, e.g., n parallel- connected battery packs of m-series-connected cells.
  • a cell's nominal capacity (NC) to 3602.7mAh, assuming that all cells in the battery pack have the same characteristics, unless otherwise specified.
  • the cell's terminal and cutoff voltages are set to 4.06267V and 2.00000V respectively.
  • BU, TD and MD policies calculate the time period during which cells are connected to the load. This calculation is based on remaining SoCs of individual cells. The higher the accuracy, the shorter the time period. With reference to Figures 8A-8C, when the cell-imbalance over series-connected cells is detected for the first time, it takes 34 seconds for cell balancing in both the BU and TD policies, and this period decreases by 24% for the second time, while the MD policy gets a 38% decrease. In the case of both the BU and TD policies, over time, the cell-imbalance is quickly mitigated, decreasing the time period for it.
  • Cell 2 acts as a base line throughout the operation-time. This means that it is relatively discharged faster (or always at a lower SoC level) than the other four, implying that cell 2 may be the weakest of all, which can be hypothetical ⁇ inferred from the fact that the weaker the battery cell, the smaller capacity it has. Accordingly, the analysis of the time periods allows one to detect an anomalous health condition of cells.
  • the load demand may be constant during the operation-time, the discharge imposed on individual cells can vary over cell balancing-when some cells are disconnected to the load, others supply demanded power with higher current. Briskly pulling the current out of the limited number of series-connected cells incurs stress on them.
  • One explicit effect caused by the imposed stress is a voltage drop. The lower the output voltage, the more the current is required from them. Besides, such intensive discharge has a negative impact on a battery's state of health. From this perspective, the TD policy may not be suitable for a high load demand.
  • the discharge rate of individual cells is in the range of 2.9 and 4.9C, which approximately equals 6 and 10 times higher than the initial discharge rate (i.e., 0.5C). Although it takes as small as 1.5 seconds in most cases, it becomes critical at a low SoC level.
  • the BU policy on the other hand, such brisk discharge is limited to only the cell last connected to the load, and the time period in most cases falls to 0.4 second, which corresponds to the upper group (of the BU policy) in the figure.
  • the cells In the lower group, the cells (excluding the last) are discharged at smaller than 2C for 1.7 seconds in most cases.
  • the MD policy forms two separate groups, but the lower group is 3 times more populated than the upper group. In the lower group, cells are discharged at 1.2C for 1.1 seconds in most cases.
  • Figures 9A-9C show histograms of the results. Interestingly enough, the BU and TD policies are featured with a widely-spread distribution, while the MD policy is with a narrow-banded distribution that has the mean of 0.9952 (C) and the variance of 0.0350 with the outliers excluded. This distribution confirms that the MD policy is best in terms of battery health regardless of a load demand.
  • the MD policy takes the median of the differences to calculate the time period for cell balancing. Although this median helps mitigate the stress imposed on individual cells, it may likely give rise to immediate need for cell balancing, increasing the overhead for it accordingly.
  • the MD policy causes 4.8 times more cell-balancing instances than either the BU or TD policy.
  • the BU or TD policy avails itself of a lightweight cell balancing at the cost of the stress. Consequently, an appropriate choice of combined policies, considering a tradeoff between the lightweight cell balancing and the performance overhead, should be made given a load demand and battery cell condition.
  • the battery-cell characteristics model is reference-based and captures the discharge and recovery efficiency. Based on the model, four scheduling mechanisms are comparatively evaluated. For this comparison to be effective, it is important to evaluate the accuracy in its simulation of battery activities.
  • a discharge profile is synthetically specified such that the workload consists of charge at rate C for 10 minutes, rest for 2 minutes, charge at rate C for 10 minutes, and charge at rate 2C for 20 minutes; this is sufficient to show the discharge and recovery efficiency.
  • the discharge profile is then ported to our battery management emulator and Dualfoil, thus yielding voltage curves over the operation-time as shown in Figures 1 OA and 1 OB. The two voltage curves are almost identical except for the turning points at which the voltage drops steeply.
  • the adaptive filter used in our scheduling framework is based on the RLS algorithm that recursively calculates an estimate from a given history of real outputs.
  • Individual elements in the history are weighted separately; we train w with 50 samples, which converges to [0.1012, 0.1916, 0.0708, 1.0377, - 0.3881 ] ⁇ .
  • the exponential forgetting factor A is set to 0.999, making it less sensitive to recent samples.
  • kRR and nRR are load-shared. That is, d is distributed to parallel-connected cells available in proportion to their remaining SoC level. By contrast, individual cells are discharged, one at a time, in both 1 RR and 1+ 1 RR. Thus, 1 RR and 1+ 1RR are likely to make individual cells overloaded and hence exhausted.
  • Figure 1 1 compares the operation-time gains of kRR, 1RR, 1 + 1RR, and nRR with the discharge profiles generated from equations (19) and (20). Applying kRR, the battery pack lasts up to 44% longer than 1 RR, 56% longer than 1 + 1RR, and 7% longer than nRR. nRR outperforms 1 RR and 1 + 1RR by 41 % and 54%, respectively. 1 + 1RR performs as ineffectively as 1RR. Thus, kRR and nRR are suitable for heavy workloads.
  • kRR acts as 1 RR which is most effective.
  • electrochemical dynamics inside the cell are inefficient in converting chemical energy to electrical energy although the effect is subject to individual cell characteristics.
  • ⁇ RR is found to perform better at a low discharge rate, showing a 13% performance gain.
  • ⁇ RR loses its performance since the recovery efficiency lags behind the discharge rate.
  • kRR's performance is excellent, because kRR effectively determines the right number of parallel-connected cells to accommodate the increasing workload while attempting to make best of their recovery efficiency.
  • kRR's scheduling performance is, on average, as good as nRR's. In other words, as few parallel-connected cells as 4 are insufficient for kRR to exploit v. As the workload becomes even heavier (Phase 4), more parallel-connected cells are required since not enough time is spend in offsetting a significant voltage drop. Obviously, such a large voltage drop can be handled by using more parallel-connected cells.
  • kRR acts as nRR. kRR maximizes the battery usability by scheduling the charge, discharge, and rest activities while adapting itself to the varying workloads.
  • ⁇ G is a threshold according to which parallel-connected cells are partitioned into G H and G L - The cells whose SoC level is below ⁇ G are put into G L - This classification is to prevent the cells from suffering the entire terminal voltage drop of the battery pack due mainly to instantaneous high loads at their low SoC level.
  • G L serves as a buffer in which the charge is reserved and then delivered to accommodate light loads. The size of the buffer, however, should be adjusted to the load; the higher the load, the larger the value of 6G.
  • the dynamic adaptation of ⁇ G supports the schedulability and hence extends the battery pack's operation-time.
  • Figure 12 shows the operation-time gains by applying the dynamic 6G.
  • kRR manages to keep the difference in voltage under 0.5%, while 1RR is subject to various loads and the cells' SoC level since a single cell should accommodate the whole load at a time. For instance, at low SoC levels of the cells, the difference in their voltage goes up to 2.5% for the 1 RR scheduling. Obviously, voltage-imbalances are rarely experienced under nRR as long as all the cells of the same characteristics are discharged simultaneously at the same rate. nRR is susceptible to cell failures or anomalous voltage variations (thus unbalancing voltages). Despite the weighted discharge, nRR experiences voltage imbalances, while kRR and 1RR quickly suppress it up to 50%.
  • a set of battery cells connected in series may be thought of as a single battery cell capable of being discharged or charged as a single unit.
  • a battery pack may consist of multiple sets of series chains of battery cells as well.
  • module may refer to, be part of, or include an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and/or memory (shared, dedicated, or group) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
  • ASIC Application Specific Integrated Circuit
  • processor shared, dedicated, or group
  • memory shared, dedicated, or group

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Secondary Cells (AREA)
  • Battery Mounting, Suspending (AREA)

Abstract

La planification effective des activités de charge et de décharge de batterie, en optimisant les caractéristiques de la batterie, peuvent étendre le temps de fonctionnement et la durée de vie du bloc-batterie. L’invention concerne un système et un procédé pour planifier les activités de la batterie. Ce cadre adapte de façon dynamique les activités de batterie aux demandes de charge et à la condition des cellules de batterie individuelles, augmentant ainsi le temps de fonctionnement du bloc-batterie et les rendant plus robustes à des déséquilibres de tension anormaux. Le cadre de planification comprend deux composants. Un filtre adaptatif estime la demande de charge entrante. Sur la base de la demande de charge estimée, un ordonnanceur peut déterminer le nombre de cellules de batterie connectées en parallèle qui doivent être déchargées. L’ordonnanceur divise également de façon efficace les cellules de batterie dans un bloc, permettant à celles-ci d’être simultanément chargées et déchargées en coordination avec un circuit de batterie reconfigurable.
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KR20120089255A (ko) 2012-08-09
KR101726249B1 (ko) 2017-04-26
WO2011014667A3 (fr) 2011-05-26
US8508191B2 (en) 2013-08-13
JP2013501488A (ja) 2013-01-10
WO2011014667A2 (fr) 2011-02-03
US20110025258A1 (en) 2011-02-03
EP2460252B1 (fr) 2018-09-12
JP5635608B2 (ja) 2014-12-03
EP2460252A4 (fr) 2016-03-30

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